Quantum Machine Learning Book Published !


Related activities

Optimal quantum key distribution networks: capacitance versus security

The rate and security of quantum communications between users placed at arbitrary points of a quantum communication network depend on the structure of the network, on its extension and on the nature of the communication channels. In this work we propose a strategy for the optimization of trusted-relays based networks that intertwines classical network approaches and quantum information theory. Specifically, by suitably defining a quantum communication efficiency functional, we identify the optimal quantum communication connections through the network by balancing security and the quantum communication rate. The optimized network is then constructed as the network of the maximal quantum communication efficiency connections and its performance is evaluated by studying the scaling of average properties as functions of the number of nodes and of the network spatial extension.


Non local solitons and dark matter in NJP !

Dark matter condensates as highly nonlocal solitons: instability in the Schwarzschild metric and laboratory analog

Theories on the bosonic nature of dark matter are a promising alternative to the cold dark matter model. Here we consider a dark matter halo in the state of a Bose–Einstein condensate (BEC), subject to the gravitation of a black hole. In the low energy limit, we bring together the general relativity in the Schwarzschild metric and the quantum description of the BEC. The model is solvable in the Fermi normal coordinates with the so-called highly nonlocal approximation and describes tidal deformations in the condensate wave function. The black hole deforms the localized condensate until the attraction of the compact object overcomes the self-gravitation and destabilizes the solitonic dark matter. Moreover, the model can be implemented as a gravitational analog in the laboratory; the time-dependent potential generated by the galactic black hole can be mimicked by an optical trap acting on a conventional condensate. The results open the way to new laboratory simulators for quantum gravitational effects.

24 New J. Phys. 26 033001 (2024)

EIC Project HEISINGBERG launched !

The EU project HEISINGBERG has started!

This project is funded by the EIC-Pathfinder initiative of the European Innovation Council for innovative Quantum technologies.

The project leverages our Spatial Ising Machine (SPIM) device and aims at a new generation of programmable and quantum annealers.

For details, have a look at the HEISINGBERG website.

HEISINGBERG logo and website

See also

Inverse design of high-dimensional quantum optical circuits in Nature Physics !

Programmable optical circuits are an important tool in developing quantum technologies such as transceivers for quantum communication and integrated photonic chips for quantum information processing. Maintaining precise control over every individual component becomes challenging at large scales, leading to a reduction in the quality of operations performed. In parallel, minor imperfections in circuit fabrication are amplified in this regime, dramatically inhibiting their performance. Here we use inverse design techniques to embed optical circuits in the higher-dimensional space of a large, ambient mode mixer such as a commercial multimode fibre. This approach allows us to forgo control over each individual circuit element, and retain a high degree of programmability. We use our circuits as quantum gates to manipulate high-dimensional spatial-mode entanglement in up to seven dimensions. Their programmability allows us to turn a multimode fibre into a generalized multioutcome measurement device, allowing us to both transport and certify entanglement within the transmission channel. With the support of numerical simulations, we show that our method is a scalable approach to obtaining high circuit fidelity with a low circuit depth by harnessing the resource of a high-dimensional mode mixer.


Highlight in Phys.org

See also Quantum Gates by TensorFlow and Reservoir Computing